SST-2 is a binary single-sentence classification task consisting of movie reviews with corresponding sentiment [1]}. MRPC is a collection of sentence pairs from online news with labels suggesting whether the pair is semantically equivalent or not [2]}. We use the final hidden vector of the special [CLS] token of BERT as the input to a linear layer, which serves as the sentence classifier. The ID of the second token in the sentence is set as the input \(x\)  for the hash function \(g(\cdot )\) . Since GLUE does not include any test dataset, we use accuracy of the validation set as the evaluation metric.
